Construction of VAE-GRU-XGBoost intrusion detection model for network security

Abstract

With the advent of the big data era, the threat of network security is becoming increasingly severe. In order to cope with complex network attacks and ensure network security, a network intrusion detection model is constructed relying on deep learning technology. In order to extract and analyze network intrusion features, this study uses variational auto-encoders to extract and reduce the dimensionality of the invaded network traffic, and combines the advantages of extreme gradient boosting to perform classification tasks. Finally, a network intrusion detection model for network security is constructed by combining the gated recurrent unit. The results showed the area under the curve of the research model reached 97.48% and 95.24% in the KDD99 dataset and OODS dataset, respectively. In the confusion matrix experiment, the model achieved classification accuracy greater than 0.91 for different attack traffic samples in both the training and testing sets. When the sample sizes were 10000 and 40000, the shortest time and longest feature extraction time of the model were 0.030s and 0.112s, respectively. In summary, the constructed model on the basis of improved variational auto-encoder for network security has high accuracy in network intrusion detection.